How to Perform Independent Rolling of Matrix Rows Using Advanced Indexing in NumPy?

Rolling Matrix Rows Independently using Advanced Indexing

Given a matrix A and an array r containing roll values for each row, your goal is to roll each row of A independently using those roll values.

The most efficient approach to achieve this is through advanced indexing in NumPy. This technique involves constructing a new meshgrid that applies the roll values to the columns of A. Here's how you can implement it:

<code class="python">import numpy as np

# Define the matrix A and roll values r
A = np.array([[4, 0, 0],
              [1, 2, 3],
              [0, 0, 5]])
r = np.array([2, 0, -1])

# Create a meshgrid of rows and negative shifted columns
rows, column_indices = np.ogrid[:A.shape[0], :A.shape[1]]
r[r < 0] += A.shape[1]
column_indices = column_indices - r[:, np.newaxis]

# Use advanced indexing to apply the roll values
result = A[rows, column_indices]

# Print the result
print(result)</code>

This approach uses negative shifted column indices to ensure valid indexing and applies the roll values through the meshgrid, resulting in a matrix with independently rolled rows.

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